Complex, industry-wide challenges such as sustainable production require collaboration across various industry actors, such as manufacturers, automation system providers and engineering technology suppliers, as well as across their related systems. While the digital twin paradigm offers immense potential through virtual asset representations for advanced predictions and analytics, building digital twins that are shared across organization boundaries and interfacing with the existing systems is not easily achieved. In fact, lack of interoperability of asset digital twins has been recognized as a growth inhibitor as well as limiting reaching the full potential and maturity of digital twins.As a solution, we present a modular, interoperable digital twin architecture named CRADLE, short for Cross-Vendor Asset Digital Twin Architecture to enable collaborations across organizational boundaries and illustrate it via an application to paper manufacturing. The industrial interoperability standards of Asset Administration Shell (AAS), Functional Mock-Up Interface (FMI), OPC UA and AutomationML, are suitably combined for integration with multiple industrial production systems as well as different cloud platforms. Use cases of energy optimization and emissions reduction, operational planning and material flow optimization are enabled through the cross-vendor asset digital twin and illustrated in the context of paper manufacturing.

Sustainability considerations and Environmental Product Declarations (EPD) are increasingly imposed by legal frameworks to reduce emissions and ensure compliance. Initiatives such as the Digital Product Passport aim at uniformly and digitally representing data for transparency and facilitated use throughout the product’s lifecycle. A challenge remains in the heterogeneity of data and formats, as well as in the assessment and comprehension of the environmental indicators by non-domain experts. Focusing on the early design phase, a potential lies in relating product design to potential environmental impact within a semantic representation.In this contribution, we explore the use of the Asset Administration Shell (AAS) during the early design phase to incorporate sustainability criteria and enhance their comprehensibility. We focus on variant generation and management inspired by software product line and by representing the AAS, along with additional domain knowledge, in the form of a knowledge graph. We present an EPD submodel as part of the AAS. Its RDF-based representation allows for property querying for direct comparison across various AAS submodel instances. The concept is demonstrated for the design of a sustainable switchgear.

AutomationML (AML), a standard for the interoperable exchange of engineering data in industrial automation, holds promise for facilitating seamless integration and data exchange across diverse industrial systems. By serializing AML to JSON, the format aims to enable a wide array of functionalities associated with JSON. Yet, such adoptions need to be carefully considered in order to have the support of the modeling community. This paper presents the efforts of the AML-JSON working group to define this serialization. It discusses the methodology employed, the technical aspects of the serialization process, and the implications for industrial applications. Furthermore, it highlights the advantages and challenges of adopting a JSON serialization within the context of AML, offering insights for researchers, practitioners, and industry stakeholders seeking to leverage this technology for enhanced data handling and interoperability in industrial automation.

An open reference architecture is presented for sustainable papermaking based on a combined usage of open industrial interoperability standards such as the Asset Administration Shell, Functional Mockup Interface, AutomationML and OPC UA for modeling and exchanging information towards decarbonization of paper production. The models combine with containerized calculation modules and end-user services, extending the capabilities of existing production systems to support development of a scalable, modular, flexible, and interoperable framework for usage by various manufacturers. The vendor-neutral, domain-agnostic architecture can also be viewed as a blueprint for sustainable manufacturing in other industrial contexts.

Composite asset or systems engineering requires integration and verification of the constituent asset models at the system level. It requires integrating relevant asset data and dynamical models of the individual assets which may be developed using diverse design and simulation tools by multiple vendors. Key integration challenges of lack of interoperability among corresponding data and simulation models, and separated, inflexible application deployments are addressed in this paper via application to a drive-motor-load system. A novel cloud-enabled drive-motor-load simulation platform based on the usage of Asset Administration Shells (AASs) for automatic data exchange and Functional Mockup Units (FMUs) for interoperable simulation is presented, together with an initial application of containerization for securing components. The solution enables higher quality and efficiency of engineering through simulation-based asset dimensioning, what-if scenario simulations, and simulation-based calibration and optimization as well as flexible, user-friendly, and more secure application deployment. An AAS-based model integration for linking various domain models and their parameters across design, configuration, and virtual commissioning, including application-side load FMUs via AAS Simulation Submodels at the composite system level, enables data interoperability while also serving as a building block of the future drive-motor-load system’s digital twin. The solution enables various applications across different product lifecycle phases such as pre-sales (drive) customer engagement, simulation-based calibration and design optimization during engineering, advanced system validations and testing in virtual commissioning, and continuous customer and OEM trainings during commissioning and operations.

A Metamodel-based Cyber-Physical Production System (CPPS) Integration Framework is presented for building comprehensive models of cyber-physical production systems with information integration from various software based on flexible interoperability alignments without requiring time-consuming and deadlock-prone semantic standardization efforts. The framework facilitates building of flexible, extensible networks of loosely coupled, multi-vendor production-level software, simulation, analytics, and visualization tools for holistic analyses, enabling collaborative innovation of production systems. At its core lies an AutomationML-based Information Metamodel which together with a simulation forms the production system's digital twin. The framework is illustrated through its application to mining production systems.

The lifecycle data of industrial devices is typically maintained in separate data sources operating in silos. The lack of interoperability between the data sources due to the usage of different APIs, data formats and data models results in time-consuming and error-prone manual data exchange efforts. The notion of digital twins is known to be a solution to the data silo problem and the associated interoperability issues. Despite many studies on digital twins, there is still a need for common architectures that offer means for defining digital twins, ingesting backend data in the digital twins, and enabling interoperable data exchange across the lifecycle of the devices via their digital twins. This paper aims to close this gap by proposing a cloud-based architecture, a common information meta-model for defining the digital twins, and variety of APIs to query the lifecycle data of interest via the digital twins.